AI Development Lesson 7: LLMs & OpenAI API

Large Language Models (GPT-4, Claude, Gemini) are general-purpose AI. Via API, you can add AI capabilities to any app in minutes.

OpenAI API

# pip install openai
from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")  # from openai.com

# Basic chat
response = client.chat.completions.create(
    model="gpt-4o-mini",  # affordable model
    messages=[
        {"role": "system", "content": "You are a helpful coding tutor."},
        {"role": "user", "content": "Explain Python decorators simply."}
    ]
)
print(response.choices[0].message.content)

# Streaming (response appears word by word)
stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role":"user","content":"Write a Python function"}],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Structured Output

import json

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{
        "role": "user",
        "content": "Extract: name, email, phone from: 'John Smith, john@example.com, 555-1234'. Return JSON only."
    }],
    response_format={"type": "json_object"}
)
data = json.loads(response.choices[0].message.content)
print(data["name"])  # "John Smith"

Groq API (Free & Fast)

# Groq is FREE and 10x faster than OpenAI!
# pip install groq
from groq import Groq

client = Groq(api_key="YOUR_GROQ_API_KEY")  # free at console.groq.com

response = client.chat.completions.create(
    model="llama-3.1-70b-versatile",
    messages=[{"role":"user","content":"Hello!"}]
)
print(response.choices[0].message.content)